It is of great practical importance to compare and combine data from different studies in order to carry out appropriate and more powerful statistical inference. We propose a partition based measure to quantify the compatibility of two datasets using their respective posterior distributions. We further propose an information gain measure to quantify the information increase (or decrease) in combining two datasets. These measures are well calibrated and efficient computational algorithms are provided for their calculations. We use examples in a benchmark dose toxicology study, a six cities pollution data and a melanoma clinical trial to illustrate how these two measures are useful in combining current data with historical data and missing data.

中文翻译：

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新的基于分区的数据兼容性和信息增益措施

比较和组合来自不同研究的数据以进行适当和更强大的统计推断具有重要的实际意义。我们提出了一种基于分区的度量，以使用它们各自的后验分布来量化两个数据集的兼容性。我们进一步提出了一种信息增益度量来量化组合两个数据集时的信息增加（或减少）。这些措施经过良好校准，并为其计算提供了有效的计算算法。我们使用基准剂量毒理学研究、六个城市的污染数据和黑色素瘤临床试验中的示例来说明这两项措施如何在将当前数据与历史数据和缺失数据相结合时发挥作用。